What are the Top 5 Visualizations of All Time?

A post from Andy Cotgreave (pictured below), Tableau Senior Product Consultant, recapping one of his presentations from the 2012 Tableau Customer Conference last week:

Data visualization has a long history, but there are certain visualizations which have impacted data visualisation and society more than any others. Here's my breakdown of the top 5 visualizations of all time.

When considering these visualisations, you should consider what they can teach us today as we sit at our desks using Tableau. Each of these visualisations have common themes:

They were designed to create change. This is what we are trying to do with our own work using Tableau. Whether it's improving Sales or finding cures for diseases, visualisations help people make decisions based on data.

The people who created these visualizations were passionate. In order to make change, you need passion too.

The visualisation is not the whole story. A visualisation itself can not stand alone. The change achieved by these visualisations came about because their designers went out and pushed their views, supported by these visualisations. If you want to make change, your visualisation also needs to be promoted by you.

These vizzes, old and new were hand-crafted or hand-coded and these authors had to make a decision based on every single design decision. Axis thickness, colors, layout, scales, etc – they drew them all themselves. When you use Tableau, consider the decisions these authors had to make and use this knowledge to make your own vizzes better.

In August 1854, a cholera outbreak killed 500 people in ten days in Soho, London. It was caused by people taking water from an infected pump on Broad Street. John Snow's map, drawn after the event, shows the distribution of the people who dided during the outbreak. More details can be found on Wikipedia, and in Steven Johnson's The Ghost Map.
This map that changed the way the world understood cholera. It forced London to realise it needed to build a sewage system to fix itself and end cholera outbreaks. This map makes my top 5 because it was used to convince people of the need for change.

Rosling is a Swedish doctor, academic and statistician. When he began teaching global development to Swedish students he realised he needed better ways to communicate the changing face of global health. The best way to understand this is to just watch him in action here. To cut to the chase, start the video at 1 minute, 40 seconds.

What makes it work? The data is great. The visualization is great. But it’s Hans Rosling who makes it work. He is amazing – he animates any story – he guides the user around.
In Tableau we can do annotations, tooltips, highlights, etc to partially emulate this. But also YOU can do what Hans does. You might have to be the one standing in front of your work, talking about it, explaining it, and adding context to it.

In 1812, Napoleon marched to Moscow in order to conquer the city. It was a disaster: having started with arou nd 470,000 soldiers, he returned with just 10,000. This chart tells the story of the campaign and is one of the most famous visualisations of all time. More information about Minard and this chart can be found on Wikipedia.

The map draws you in and encourages you to linger. It invites exploration and reflection. If your goal is to get someone to engage and digest, then Minard’s approach works in an unmatchable way.

Because of its fame, there is a lot of critical commentary about this chart (this post from Excelcharts.com is a good example). A lot of it is reasonable, but this remains a hugely influential and successful chart. Think back to when you first saw this chart – that moment was the light bulb in your head when you realised the power of data visualization: you can tell a story without just using words. And you can draw a chart that captures huge amounts of information.

At number 2 is a viz that, like the Cholera Map, led to change. Not only that, it shows that although you should always strive for visual best practise in your visualizations, sometimes that can be less of priority than communicating your message. Who was Florence Nightingale? She was a total Data Rockstar!. She’s mostly famous for being the “lady of the lamp” but her contribution to the world of data is significant.
The charts were part of a Royal Commission looking into the causes of mortality of soldiers in the Crimean War. Nightingale worked with William Farr, a Victorian pioneer in statistics. He did not support the idea of the visualisations being included in the report, but Nightingale stood firm, knowing that people need to see data in order to understand it.

This chart encapsulates everything you should be striving for as a visual analyst. It was innovative and original. It was seen by thousands of people and designed as a way to display a huge amount of information in a digestible, interesting manner. It also helped change the world of data visualization.
So why is this chart the most influential of all time? Not just because of the huge audience this visualisation reached. Not just because of the innovations in this chart. It is primarily because this chart directly influenced William Playfair, who used Priestley's techniques to develop some of the first ever statistical line charts:

What can we take away from the top 5?

There are two conclusions to be drawn from this list:
Improve your Tableau Design skills. Every design decision: pixels, axes, colours, mark types. All were thought about and considered. Tableau makes these choices for you a lot of the time, but you should always consider the visualizations you are making.

You too can be influential. Visualizations can change the world. What I learned in researching this is that a visualisation itself is not the main influencer. They each said “I want to change things for the better. And I have the data to prove it.” And that’s what we can learn from these pioneering, passionate designers.
What do you think? Do you agree? To continue the conversation, comment below, or find me on Twitter @acotgreave. I look forward to your comments and opinions.
Do you want to find out more? Check out this list of resources: http://bit.ly/5vizzes

Comments

This was a great talk. The "Big Ideas" track at TCC12 was spectacular! Would love to see a blog post recapping each of those sessions.

Submitted by Richard G. on November 27, 2012 - 9:05am

Andy, thanks for sharing this talk. I wanted to see it at the conference, but I was focusing on the hands-on sessions. I've never seen Joseph Priestley's chart. Thanks for turning me on to that. The other four, I'm familiar with, but I still would have liked to have heard your discussion.

A question to ask when opening data in Tableau:
Am I communicating information, displaying an analysis, or exploring data?

For me, these examples from Snow, Minard, Nightingale, and Playfair are communicating information, Priestley's is displaying an analysis, and Gapminder and Tableau enable all three.

Submitted by Richard G. on November 29, 2012 - 8:06am

I like your question, "Am I communication information, displaying an analysis, or exploring data?" I've never thought about that in my work with Tableau, but it makes sense. I love it when a concept can be captured by a guiding question.

Can you talk a little bit about the difference between "communicating information" and "displaying an analysis"? Is Priestley's chart displaying an analysis because he's not trying to communicate or persuade us of anything? He's laying out the lives of important people through time, but he's not arguing a point. Snow and Nightengale, however, were trying to persuade an audience.

Am I understanding you correctly?

Submitted by Joe M. on November 29, 2012 - 8:26am

Richard,

I agree, and also for the Snow and Nightengale visualizations, the message was decided before the view was created.

In a way, my question is to ask myself am I letting the data speak to me, or am I shaping it to fit a pre-determined story.

Communicating information: You have an agenda or a message to tell others. You design a viz specifically intended to get that message across.
"Displaying an analysis": I'd call this "Guided Analytics". In this case, you design an analytical app that allows the user to explore and find things out for themselves. This is a walled garden in a way, in that the audience can only explore within the functionality that you've provided.
Exploring data: you don't have a specific thing to prove at the start of your visualisation. You are using a tool like tableau to find meaning in your data. All you need for that is the exploratory tool, and some curiosity.

I'd classify my five choices as follows:
Communicating information: Snow, Nightingale, Minard, Priestley
Guided Analytics: Rosling
I might argue that Priestley is guided analytics. There's no interactivity, but you can explore the data in different ways.

@Andy Cotgreave Sorry I didn't see/hear the session, but I just ran into http://www.slideshare.net/TableauSoftware/the-5-most-influential-data-visualizations-of-all-time and didn't see any credit there. I presumed it was a pretty obvious "copy" of the examples that Tufte used in his books. Plus, 4/5 visualizations here was strikingly similar to Tufte's. That was my first thought :)